Data science isn’t just changing web design in a minor way: it’s changing every aspect of it from the start of the design process to the end (and even beyond through the update process). Whenever you have the resources and expertise to deploy it, it’s worthwhile, because having cut-and-dry insight into performance is invaluable.
When the internet first began to pick up some steam, it had some of the hallmarks of the Wild West: the rules hadn’t been clearly defined, for instance, and direct competition was fairly minimal because there was so much space to be filled. As a result, there was a lot of freewheeling experimentation going by gut feeling. Just try something, see how it goes.
As the years have gone by, the online world has gone from an attention-grabbing novelty to a fundamental part of daily life. Along the way, its standards have changed immensely, and there are two reasons for this: technological progress in general, and frequent efforts from online brands to exceed previous levels of service and performance to outperform their rivals.
Given how competitive the web is now, going by gut feeling won’t get brands very far, so they are essentially required to invest in data science: using the smartest methods available to them to analyze relevant data and reach informed conclusions about what they need to do. In this post, we’re going to consider how data science is changing web design. Let’s begin:
The significance of data science is such that it doesn’t simply factor into one element of the web design process (weighing in at the end, perhaps) — instead, it has a key role to play at every point and in every department. The reasoning for this should be fairly obvious. Given that every step in a digital process inevitably produces trackable results (without trackable results, you can’t know how well something is working), the opportunity for data science is always there.
It may not be used at all opportunities when small businesses run web design projects, but that’s simply because they don’t have the resources to invest so broadly. Look at big companies to see where things are going. Case in point: eCommerce giant Shopify (known for its sell everything everywhere message) has a full Data Science & Engineering team, but it doesn’t serve exclusively as a separate unit.
Instead, as department VP Solmaz Shahalizadeh said in this interview with Matt Turck, “Data scientists are embedded in different business units or product areas. They work closely with the product managers, UX researchers and development teams.” By saturating the design process with data scientists, the department ensures that feedback has maximum utility.
Putting so much time and effort into data science wouldn’t be worthwhile if it didn’t ultimately get impressive results, so we need to think about the net product of the investment: improved user experiences. Excellent web design needs to allow users to find what they need with minimal effort and optimal convenience. In addition to being easy to operate, it should find other ways to impress the user: the bigger a positive impression it can make, the better.
Now think about all the customized and personalized experiences you can now get in the online world. You can visit a familiar retailer and benefit from dynamic recommendations that factor in your previous purchases, deals that suit your interests, and even design elements that reflect your preferences (not common, but not unheard of).
All of these things are made possible by the data science field: without machine learning to automate them, dynamic recommendations would need to be done manually. You can even think about chatbot-enhanced customer support. Being able to check up on the status of an order by issuing a request through a chatbot window feels like a trivial thing, but it was made possible by advances in natural language processing that couldn’t have been achieved without deploying machine learning to process and parse vast quantities of data.
Web designs aren’t supposed to be static. Once you deploy them, it won’t be very long before they’re outdated relative to newer designs, so you need to make a commitment to keeping them updated. But what if you didn’t need to slowly work on manual updates and carefully roll them out as appropriate? What if you could automate much of the update process?
Well, with the right process in place (powered by data science, of course), you could. You could set up a system with various flexible elements and have it reshuffle those elements in response to analytics from testing. Tweak one element and see how the results change: if they get worse, revert the tweak, and if they get better, leave it as it is and tweak something else.
In the long run, self-optimizing systems are going to become extremely common. Human intelligence can then be put towards more interesting things, such as new projects or updates that go beyond basic comparison testing. The key will be finding a balance between the things at which computers excel (repetitive processes at massive scale) and the things at which human brains excel (complex issues requiring creative thinking).